Courses

Applied Deep Learning

This course will cover the fundamental concepts related to deep learning. Topics include:

Prerequisites: Programming skills up to data structures and a senior/graduate level course in statistics. Knowledge of Python and Linux.

Time Place

Tuesday 12-3 pm on Zoom. Zoom Meeting Room ID and code on Blackboard

Textbook

Getting Started with Deep Learning: Programming and Methodologies using Python
By Ricardo Calix, Ph.D.

Instructor

Ricardo A. Calix, Ph.D.
Purdue University Northwest
rcalix@pnw.edu

Office Hours

My office is at 241 Anderson

On-Line Office Hours

Thursday 2-4 pm (or by appointment) on Zoom. Zoom Meeting Room ID and code on Blackboard

About Purdue University Northwest

Code

GitHub
 

Videos

Blackboard (Submit homework on Blackboard)

Blackboard

Related Papers

Lab Environment

Environment:

AWS

Course Materials

Labs

  1. More materials on Blackboard

Tools

We will use the following software:

  1. Linux
  2. Python
  3. Anaconda

Calendar Spring 2020 (Subject to change)

Mon Tue Wed Thu Fri
Jan 13
Jan 14

Tensorflow
Data for Tensorflow
(Video1) (Video2)
Jan 15
Jan 16

Lab: Intro to Tensorflow
Jan 17
Jan 20
Jan 21
Introduction to Deep Learning
(Video)
Jan 22
Jan 23
Lab: Tensors and Tensorflow basics
Performance metrics
(Video)
Jan 24
Jan 27
Jan 28
Linear Regression
(Video)
Jan 29
Jan 30
Lab: Linear Regression
Jan 31
Feb 3
Feb 4
Linear Regression to Logistic Regression
(Video)
Feb 5
Feb 6
Lab: Logistic Regression
Feb 7
Feb 10
Feb 11
Logistic Regression
(Video)
Feb 12

break
Feb 13
Lab: Optimization Functions - LSE, Cross Entropy
Feb 14

break
Feb 17
Feb 18
Neural Networks
Feb 19
Feb 20
Neural Networks
Feb 21
Feb 24

lunch
Feb 25
Deep Neural Networks
(Video1)
Feb 26

lunch
Feb 27
Lab: Deep Neural Networks
Feb 28

lunch
Mar 2

lunch
Mar 3
MNIST and Deep Neural Networks
Mar 4

lunch
Mar 5
Lab: MNIST and Deep Neural Networks
Mar 6

Mar 9

break
Mar 10

Auto Encoders
Mar 11

break
Mar 12

Lab: Auto Encoders
Mar 13

break
Mar 16

break
Mar 17

break
Mar 18

break
Mar 19

break
Mar 20

break
Mar 23

break
Mar 24

CNNs
(Video)
Mar 25

break
Mar 26

Lab: CNNs
(Video)
Mar 27

Mar 30
Mar 31
Mid-Term exam
(Video) (slides)
Apr 1
Apr 2
Mid-Term exam
Apr 3
Apr 6
Apr 7
CNNs with RGB images
(Video1)
(Video2)
(code)
Apr 8
Apr 9
Lab: CNNs with RGB images
Apr 10

Apr 13
Apr 14
Recurrent Neural Networks and MNIST
(Video1)
(Video2)
Apr 15
Apr 16
Lab: Recurrent Neural Networks and MNIST
Apr 17
Apr 20

Apr 21
Recurrent Neural Networks and NLP, N-grams
(Video)
Apr 22

Apr 23
Lab: RNNs and NLP
Apr 24

Apr 27
Apr 28
Presentations
(Video)
Apr 29

Apr 30
Presentations
May 1

May 4
Finals
May 5
Finals
May 6
Finals
May 7
Finals
May 8
Finals